Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory121.3 B

Variable types

Numeric14
Categorical1

Alerts

Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2024-03-03 12:28:15.227213
Analysis finished2024-03-03 12:29:09.651589
Duration54.42 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.5
Minimum0
Maximum99
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:09.862747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.95
Q124.75
median49.5
Q374.25
95-th percentile94.05
Maximum99
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.58609075
Kurtosis-1.2
Mean49.5
Median Absolute Deviation (MAD)25
Skewness0
Sum4950
Variance841.66667
MonotonicityStrictly increasing
2024-03-03T12:29:10.270973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1
 
1.0%
0 1
 
1.0%
1 1
 
1.0%
2 1
 
1.0%
84 1
 
1.0%
85 1
 
1.0%
86 1
 
1.0%
87 1
 
1.0%
88 1
 
1.0%
89 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0 1
1.0%
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
ValueCountFrequency (%)
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%

target
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
1
59 
2
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Length

2024-03-03T12:29:10.672624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T12:29:10.964641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Most occurring characters

ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

alcohol
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.1984
Minimum11.62
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:11.353275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum11.62
5-th percentile11.8385
Q112.37
median13.295
Q313.8375
95-th percentile14.3705
Maximum14.83
Range3.21
Interquartile range (IQR)1.4675

Descriptive statistics

Standard deviation0.8259402
Coefficient of variation (CV)0.062578812
Kurtosis-1.0166013
Mean13.1984
Median Absolute Deviation (MAD)0.615
Skewness-0.23732785
Sum1319.84
Variance0.68217721
MonotonicityNot monotonic
2024-03-03T12:29:11.781022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 5
 
5.0%
12.37 5
 
5.0%
12.29 4
 
4.0%
12.08 3
 
3.0%
11.84 2
 
2.0%
13.83 2
 
2.0%
12.33 2
 
2.0%
12 2
 
2.0%
14.1 2
 
2.0%
13.86 2
 
2.0%
Other values (66) 71
71.0%
ValueCountFrequency (%)
11.62 1
 
1.0%
11.64 1
 
1.0%
11.65 1
 
1.0%
11.66 1
 
1.0%
11.81 1
 
1.0%
11.84 2
2.0%
11.96 1
 
1.0%
12 2
2.0%
12.08 3
3.0%
12.16 1
 
1.0%
ValueCountFrequency (%)
14.83 1
1.0%
14.75 1
1.0%
14.39 1
1.0%
14.38 2
2.0%
14.37 1
1.0%
14.3 1
1.0%
14.23 1
1.0%
14.22 2
2.0%
14.21 1
1.0%
14.2 1
1.0%

malic_acid
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8469
Minimum0.89
Maximum4.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:12.156028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.89
5-th percentile0.978
Q11.5
median1.71
Q31.9
95-th percentile3.841
Maximum4.04
Range3.15
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.73572159
Coefficient of variation (CV)0.39835486
Kurtosis2.5366139
Mean1.8469
Median Absolute Deviation (MAD)0.205
Skewness1.6599709
Sum184.69
Variance0.54128625
MonotonicityNot monotonic
2024-03-03T12:29:12.549158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 5
 
5.0%
1.81 4
 
4.0%
1.67 3
 
3.0%
1.9 2
 
2.0%
1.77 2
 
2.0%
1.68 2
 
2.0%
1.5 2
 
2.0%
1.66 2
 
2.0%
1.83 2
 
2.0%
1.53 2
 
2.0%
Other values (66) 74
74.0%
ValueCountFrequency (%)
0.89 1
1.0%
0.9 1
1.0%
0.92 1
1.0%
0.94 2
2.0%
0.98 1
1.0%
0.99 1
1.0%
1.01 1
1.0%
1.07 1
1.0%
1.09 1
1.0%
1.1 1
1.0%
ValueCountFrequency (%)
4.04 1
1.0%
3.99 1
1.0%
3.98 1
1.0%
3.87 1
1.0%
3.86 1
1.0%
3.84 1
1.0%
3.8 1
1.0%
3.59 1
1.0%
3.17 1
1.0%
3.1 1
1.0%

ash
Real number (ℝ)

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3594
Minimum1.36
Maximum3.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:12.893000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.56
95-th percentile2.721
Maximum3.22
Range1.86
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.27936371
Coefficient of variation (CV)0.11840455
Kurtosis1.5190238
Mean2.3594
Median Absolute Deviation (MAD)0.16
Skewness-0.41637027
Sum235.94
Variance0.078044081
MonotonicityNot monotonic
2024-03-03T12:29:13.269633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 4
 
4.0%
2.28 4
 
4.0%
2.36 4
 
4.0%
2.32 3
 
3.0%
2.7 3
 
3.0%
2.62 3
 
3.0%
2.21 3
 
3.0%
2.1 3
 
3.0%
1.92 3
 
3.0%
2.5 2
 
2.0%
Other values (50) 68
68.0%
ValueCountFrequency (%)
1.36 1
 
1.0%
1.7 1
 
1.0%
1.71 1
 
1.0%
1.75 1
 
1.0%
1.92 3
3.0%
1.95 1
 
1.0%
1.98 1
 
1.0%
2 1
 
1.0%
2.02 1
 
1.0%
2.04 1
 
1.0%
ValueCountFrequency (%)
3.22 1
 
1.0%
2.87 1
 
1.0%
2.84 1
 
1.0%
2.8 1
 
1.0%
2.74 1
 
1.0%
2.72 1
 
1.0%
2.7 3
3.0%
2.68 2
2.0%
2.67 2
2.0%
2.65 2
2.0%

alcalinity_of_ash
Real number (ℝ)

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.038
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:13.733357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile13.16
Q116
median17.9
Q319.425
95-th percentile24
Maximum30
Range19.4
Interquartile range (IQR)3.425

Descriptive statistics

Standard deviation3.2162711
Coefficient of variation (CV)0.17830531
Kurtosis1.6243348
Mean18.038
Median Absolute Deviation (MAD)1.85
Skewness0.67342478
Sum1803.8
Variance10.3444
MonotonicityNot monotonic
2024-03-03T12:29:14.211117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 10
 
10.0%
18 9
 
9.0%
16.8 5
 
5.0%
19 5
 
5.0%
17.2 3
 
3.0%
17 3
 
3.0%
18.8 3
 
3.0%
20 3
 
3.0%
21 2
 
2.0%
14 2
 
2.0%
Other values (46) 55
55.0%
ValueCountFrequency (%)
10.6 1
1.0%
11.2 1
1.0%
11.4 1
1.0%
12 1
1.0%
12.4 1
1.0%
13.2 1
1.0%
14 2
2.0%
14.6 1
1.0%
14.8 1
1.0%
15 2
2.0%
ValueCountFrequency (%)
30 1
1.0%
26 1
1.0%
25 2
2.0%
24 2
2.0%
23.6 1
1.0%
23 1
1.0%
22.8 1
1.0%
22.5 2
2.0%
22 1
1.0%
21.6 1
1.0%

magnesium
Real number (ℝ)

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.61
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:14.700106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.95
Q191.75
median101
Q3111
95-th percentile132.1
Maximum162
Range92
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation15.833665
Coefficient of variation (CV)0.15430918
Kurtosis1.758742
Mean102.61
Median Absolute Deviation (MAD)10
Skewness0.98328768
Sum10261
Variance250.70495
MonotonicityNot monotonic
2024-03-03T12:29:15.135976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
101 8
 
8.0%
98 5
 
5.0%
86 5
 
5.0%
88 5
 
5.0%
102 4
 
4.0%
94 4
 
4.0%
118 3
 
3.0%
96 3
 
3.0%
100 3
 
3.0%
108 3
 
3.0%
Other values (39) 57
57.0%
ValueCountFrequency (%)
70 1
 
1.0%
78 3
3.0%
80 1
 
1.0%
81 1
 
1.0%
84 1
 
1.0%
85 2
 
2.0%
86 5
5.0%
87 2
 
2.0%
88 5
5.0%
89 1
 
1.0%
ValueCountFrequency (%)
162 1
1.0%
151 1
1.0%
139 1
1.0%
136 1
1.0%
134 1
1.0%
132 1
1.0%
128 1
1.0%
127 1
1.0%
126 1
1.0%
124 1
1.0%

total_phenols
Real number (ℝ)

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5906
Minimum1.1
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:15.513396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.6095
Q12.2
median2.635
Q32.965
95-th percentile3.381
Maximum3.88
Range2.78
Interquartile range (IQR)0.765

Descriptive statistics

Standard deviation0.54829915
Coefficient of variation (CV)0.21164948
Kurtosis-0.08705305
Mean2.5906
Median Absolute Deviation (MAD)0.355
Skewness-0.29380436
Sum259.06
Variance0.30063196
MonotonicityNot monotonic
2024-03-03T12:29:15.877274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6
 
6.0%
2.8 5
 
5.0%
2.2 5
 
5.0%
2.95 5
 
5.0%
2.6 4
 
4.0%
2.85 4
 
4.0%
2.45 4
 
4.0%
3.3 3
 
3.0%
2.42 3
 
3.0%
2.98 2
 
2.0%
Other values (50) 59
59.0%
ValueCountFrequency (%)
1.1 1
1.0%
1.38 1
1.0%
1.45 1
1.0%
1.6 2
2.0%
1.61 1
1.0%
1.65 1
1.0%
1.72 1
1.0%
1.78 1
1.0%
1.85 1
1.0%
1.88 1
1.0%
ValueCountFrequency (%)
3.88 1
 
1.0%
3.85 1
 
1.0%
3.52 1
 
1.0%
3.5 1
 
1.0%
3.4 1
 
1.0%
3.38 1
 
1.0%
3.3 3
3.0%
3.27 1
 
1.0%
3.25 2
2.0%
3.2 1
 
1.0%

flavanoids
Real number (ℝ)

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5606
Minimum0.57
Maximum3.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:16.259776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile1.2785
Q12.1125
median2.68
Q33.07
95-th percentile3.6415
Maximum3.93
Range3.36
Interquartile range (IQR)0.9575

Descriptive statistics

Standard deviation0.73456896
Coefficient of variation (CV)0.28687376
Kurtosis-0.38569985
Mean2.5606
Median Absolute Deviation (MAD)0.47
Skewness-0.52432778
Sum256.06
Variance0.53959156
MonotonicityNot monotonic
2024-03-03T12:29:16.681727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.68 3
 
3.0%
2.53 2
 
2.0%
3.17 2
 
2.0%
2.26 2
 
2.0%
1.59 2
 
2.0%
3 2
 
2.0%
1.69 2
 
2.0%
2.43 2
 
2.0%
2.76 2
 
2.0%
2.65 2
 
2.0%
Other values (75) 79
79.0%
ValueCountFrequency (%)
0.57 1
1.0%
0.99 1
1.0%
1.02 1
1.0%
1.09 1
1.0%
1.25 1
1.0%
1.28 1
1.0%
1.3 1
1.0%
1.32 1
1.0%
1.41 1
1.0%
1.46 1
1.0%
ValueCountFrequency (%)
3.93 1
1.0%
3.75 1
1.0%
3.74 1
1.0%
3.69 1
1.0%
3.67 1
1.0%
3.64 1
1.0%
3.56 1
1.0%
3.54 1
1.0%
3.49 1
1.0%
3.4 1
1.0%

nonflavanoid_phenols
Real number (ℝ)

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3136
Minimum0.13
Maximum0.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:17.085004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.17
Q10.25
median0.29
Q30.37
95-th percentile0.5205
Maximum0.63
Range0.5
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.10460624
Coefficient of variation (CV)0.3335658
Kurtosis0.68792811
Mean0.3136
Median Absolute Deviation (MAD)0.05
Skewness0.89649809
Sum31.36
Variance0.010942465
MonotonicityNot monotonic
2024-03-03T12:29:18.043186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.26 8
 
8.0%
0.29 7
 
7.0%
0.32 7
 
7.0%
0.27 6
 
6.0%
0.3 6
 
6.0%
0.28 5
 
5.0%
0.22 5
 
5.0%
0.34 5
 
5.0%
0.43 4
 
4.0%
0.24 4
 
4.0%
Other values (24) 43
43.0%
ValueCountFrequency (%)
0.13 1
 
1.0%
0.14 2
 
2.0%
0.17 4
4.0%
0.19 2
 
2.0%
0.2 2
 
2.0%
0.21 4
4.0%
0.22 5
5.0%
0.24 4
4.0%
0.25 2
 
2.0%
0.26 8
8.0%
ValueCountFrequency (%)
0.63 1
1.0%
0.61 1
1.0%
0.58 1
1.0%
0.55 1
1.0%
0.53 1
1.0%
0.52 1
1.0%
0.5 2
2.0%
0.48 1
1.0%
0.47 1
1.0%
0.45 2
2.0%

proanthocyanins
Real number (ℝ)

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7696
Minimum0.41
Maximum3.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:18.452922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.939
Q11.4575
median1.765
Q32.0325
95-th percentile2.81
Maximum3.28
Range2.87
Interquartile range (IQR)0.575

Descriptive statistics

Standard deviation0.53722053
Coefficient of variation (CV)0.30358303
Kurtosis0.75778973
Mean1.7696
Median Absolute Deviation (MAD)0.305
Skewness0.014172568
Sum176.96
Variance0.2886059
MonotonicityNot monotonic
2024-03-03T12:29:18.889606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.46 5
 
5.0%
1.98 4
 
4.0%
1.35 4
 
4.0%
1.66 4
 
4.0%
2.81 3
 
3.0%
1.97 3
 
3.0%
1.62 3
 
3.0%
1.87 3
 
3.0%
2.38 3
 
3.0%
2.08 3
 
3.0%
Other values (55) 65
65.0%
ValueCountFrequency (%)
0.41 1
1.0%
0.42 2
2.0%
0.62 1
1.0%
0.73 1
1.0%
0.95 1
1.0%
1.03 2
2.0%
1.04 1
1.0%
1.15 1
1.0%
1.25 2
2.0%
1.28 1
1.0%
ValueCountFrequency (%)
3.28 1
 
1.0%
2.96 1
 
1.0%
2.91 1
 
1.0%
2.81 3
3.0%
2.76 1
 
1.0%
2.5 1
 
1.0%
2.45 1
 
1.0%
2.38 3
3.0%
2.35 1
 
1.0%
2.34 1
 
1.0%

color_intensity
Real number (ℝ)

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.606
Minimum1.74
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:19.301816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.74
5-th percentile2.395
Q13.33
median4.55
Q35.6575
95-th percentile7.224
Maximum8.9
Range7.16
Interquartile range (IQR)2.3275

Descriptive statistics

Standard deviation1.5788341
Coefficient of variation (CV)0.34277771
Kurtosis-0.34178495
Mean4.606
Median Absolute Deviation (MAD)1.16
Skewness0.37501416
Sum460.6
Variance2.4927172
MonotonicityNot monotonic
2024-03-03T12:29:19.722676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.05 3
 
3.0%
3.8 3
 
3.0%
4.5 3
 
3.0%
5.1 3
 
3.0%
4.6 3
 
3.0%
4.8 2
 
2.0%
6.2 2
 
2.0%
2.5 2
 
2.0%
5.6 2
 
2.0%
5.75 2
 
2.0%
Other values (73) 75
75.0%
ValueCountFrequency (%)
1.74 1
1.0%
1.95 1
1.0%
2.15 1
1.0%
2.2 1
1.0%
2.3 1
1.0%
2.4 1
1.0%
2.45 1
1.0%
2.5 2
2.0%
2.57 1
1.0%
2.6 2
2.0%
ValueCountFrequency (%)
8.9 1
1.0%
8.7 1
1.0%
7.8 1
1.0%
7.5 1
1.0%
7.3 1
1.0%
7.22 1
1.0%
7.2 1
1.0%
7.05 1
1.0%
6.9 1
1.0%
6.8 1
1.0%

hue
Real number (ℝ)

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.09416
Minimum0.79
Maximum1.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:20.236986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile0.8795
Q11.0075
median1.085
Q31.19
95-th percentile1.3315
Maximum1.45
Range0.66
Interquartile range (IQR)0.1825

Descriptive statistics

Standard deviation0.1405374
Coefficient of variation (CV)0.12844319
Kurtosis-0.32789889
Mean1.09416
Median Absolute Deviation (MAD)0.105
Skewness0.18406519
Sum109.416
Variance0.019750762
MonotonicityNot monotonic
2024-03-03T12:29:20.722571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1.04 6
 
6.0%
1.23 6
 
6.0%
1.12 6
 
6.0%
1.25 5
 
5.0%
1.07 4
 
4.0%
1.05 4
 
4.0%
1.09 4
 
4.0%
1.19 4
 
4.0%
1.13 3
 
3.0%
0.96 3
 
3.0%
Other values (36) 55
55.0%
ValueCountFrequency (%)
0.79 1
1.0%
0.82 1
1.0%
0.84 1
1.0%
0.86 1
1.0%
0.87 1
1.0%
0.88 2
2.0%
0.89 2
2.0%
0.906 1
1.0%
0.91 2
2.0%
0.92 2
2.0%
ValueCountFrequency (%)
1.45 1
 
1.0%
1.42 1
 
1.0%
1.38 1
 
1.0%
1.36 2
 
2.0%
1.33 1
 
1.0%
1.31 2
 
2.0%
1.28 2
 
2.0%
1.25 5
5.0%
1.24 1
 
1.0%
1.23 6
6.0%
Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9501
Minimum1.59
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:21.154596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.59
5-th percentile2.006
Q12.7275
median2.98
Q33.315
95-th percentile3.592
Maximum4
Range2.41
Interquartile range (IQR)0.5875

Descriptive statistics

Standard deviation0.49566504
Coefficient of variation (CV)0.16801635
Kurtosis0.23150191
Mean2.9501
Median Absolute Deviation (MAD)0.285
Skewness-0.55207363
Sum295.01
Variance0.24568383
MonotonicityNot monotonic
2024-03-03T12:29:21.676406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 4
 
4.0%
2.87 4
 
4.0%
3.21 2
 
2.0%
3.13 2
 
2.0%
2.26 2
 
2.0%
2.88 2
 
2.0%
3.16 2
 
2.0%
2.65 2
 
2.0%
3.33 2
 
2.0%
2.75 2
 
2.0%
Other values (66) 76
76.0%
ValueCountFrequency (%)
1.59 1
1.0%
1.67 1
1.0%
1.82 2
2.0%
1.93 1
1.0%
2.01 1
1.0%
2.06 1
1.0%
2.14 1
1.0%
2.23 1
1.0%
2.26 2
2.0%
2.27 1
1.0%
ValueCountFrequency (%)
4 1
1.0%
3.92 1
1.0%
3.82 1
1.0%
3.71 1
1.0%
3.63 1
1.0%
3.59 1
1.0%
3.58 2
2.0%
3.56 1
1.0%
3.55 1
1.0%
3.53 1
1.0%

proline
Real number (ℝ)

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean887.81
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-03-03T12:29:22.123703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile405.3
Q1609.25
median900.5
Q31135
95-th percentile1451.5
Maximum1680
Range1402
Interquartile range (IQR)525.75

Descriptive statistics

Standard deviation340.67478
Coefficient of variation (CV)0.38372488
Kurtosis-0.94990281
Mean887.81
Median Absolute Deviation (MAD)273
Skewness0.08448632
Sum88781
Variance116059.31
MonotonicityNot monotonic
2024-03-03T12:29:22.540866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1285 3
 
3.0%
1035 3
 
3.0%
680 3
 
3.0%
450 3
 
3.0%
1095 2
 
2.0%
428 2
 
2.0%
845 2
 
2.0%
630 2
 
2.0%
1150 2
 
2.0%
1280 2
 
2.0%
Other values (68) 76
76.0%
ValueCountFrequency (%)
278 1
 
1.0%
290 1
 
1.0%
345 1
 
1.0%
355 1
 
1.0%
392 1
 
1.0%
406 1
 
1.0%
410 1
 
1.0%
420 1
 
1.0%
428 2
2.0%
450 3
3.0%
ValueCountFrequency (%)
1680 1
1.0%
1547 1
1.0%
1515 1
1.0%
1510 1
1.0%
1480 1
1.0%
1450 1
1.0%
1375 1
1.0%
1320 1
1.0%
1310 1
1.0%
1295 1
1.0%

Interactions

2024-03-03T12:29:04.654908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:16.953280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:20.414681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:23.877057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:27.707018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:31.087121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:34.782519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:38.949526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:42.289117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:46.247663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:49.915137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:53.251068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:56.821980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:00.966010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:04.913712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:17.180991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:20.659851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:24.108598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:27.943524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:31.327425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:35.077743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:39.159733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:42.542010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:46.487246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:50.154679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:53.462311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:57.042038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:01.193146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:05.207525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:17.422100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:20.935987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:24.407905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:28.205832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:31.582667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:35.449941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:39.375854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:42.802335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:46.722020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:50.384657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:53.693720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:57.341925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:01.550478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:05.625851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:17.661801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:21.198289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:24.676624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:28.448522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:31.851218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:35.737965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:39.591919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:43.063654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:46.994025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:50.619223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:53.916538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:57.587082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:01.830571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:05.932763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:17.908754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:21.466521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:24.946317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:28.692363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:32.116307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:36.005589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:39.814169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:43.421579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:47.237453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:50.849444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:54.172795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:57.889102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:02.109186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:06.238965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:18.166357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:21.779489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:25.239072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:28.962014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:32.403878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:36.278204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:40.046354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:43.757569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:47.483204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:51.108745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:54.452384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:58.194404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:02.380283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:06.532013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:18.382001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:21.995556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:25.519293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:29.181306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:32.645660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:36.511981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:40.245807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:44.042499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:47.733505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:51.379719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:54.717774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:58.927976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:02.610951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:06.826317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:18.648709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:22.223246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:25.828996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:29.413235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:32.919802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:37.175269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:40.458461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:44.330521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:48.028943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:51.621735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:54.963372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:59.172845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:02.874546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:07.115391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:18.909725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:22.453563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:26.106960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:29.645010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:33.179476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:37.457943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:40.716326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:44.631349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:48.299922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:51.934740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:55.262792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:59.415287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:03.156960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:07.366648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:19.129017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:22.673835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:26.383763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:29.857137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:33.425640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:37.741208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:40.965225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:44.907526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:48.554243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:52.147710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:55.548914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:59.637609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:03.397495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:07.633311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:19.352885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:22.907965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:26.700593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:30.079990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:33.677627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:37.968502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:41.250164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:45.208423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:48.843998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:52.355636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:55.817379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:59.877246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:03.648001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:07.912584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:19.598116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:23.173407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:26.957851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:30.308531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:33.945725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:38.242163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:41.519659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:45.491588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:49.124621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:52.578537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:56.100220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:00.261851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:03.906185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:08.206966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:19.867014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:23.408278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:27.219873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:30.602240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:34.245250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:38.467718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:41.793917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:45.773988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:49.370520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:52.803412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:56.360843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:00.511965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:04.151157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:08.463469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:20.117110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:23.641212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:27.464970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:30.846583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:34.526541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:38.706004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:42.040024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:46.009279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:49.616810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:53.026746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:28:56.591779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:00.736278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T12:29:04.388007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-03T12:29:08.833551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T12:29:09.399980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0targetalcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
00114.231.712.4315.61272.803.060.282.295.641.043.921065
11113.201.782.1411.21002.652.760.261.284.381.053.401050
22113.162.362.6718.61012.803.240.302.815.681.033.171185
33114.371.952.5016.81133.853.490.242.187.800.863.451480
44113.242.592.8721.01182.802.690.391.824.321.042.93735
55114.201.762.4515.21123.273.390.341.976.751.052.851450
66114.391.872.4514.6962.502.520.301.985.251.023.581290
77114.062.152.6117.61212.602.510.311.255.051.063.581295
88114.831.642.1714.0972.802.980.291.985.201.082.851045
99113.861.352.2716.0982.983.150.221.857.221.013.551045
Unnamed: 0targetalcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
9090212.081.832.3218.5811.601.500.521.642.401.082.27480
9191212.001.512.4222.0861.451.250.501.633.601.052.65450
9292212.691.532.2620.7801.381.460.581.623.050.962.06495
9393212.292.832.2218.0882.452.250.251.992.151.153.30290
9494211.621.992.2818.0983.022.260.171.353.251.162.96345
9595212.471.522.2019.01622.502.270.323.282.601.162.63937
9696211.812.122.7421.51341.600.990.141.562.500.952.26625
9797212.291.411.9816.0852.552.500.291.772.901.232.74428
9898212.371.072.1018.5883.523.750.241.954.501.042.77660
9999212.293.172.2118.0882.852.990.452.812.301.422.83406